A major statistical problem in mental health research has been how to translate subjects' scores on a set of single dimensions into a useful classification of individuals. This problem is particularly significant since decisions (e.g., regarding treatment assignment) must be made about whole individuals, rather than scores on single dimensions. The purpose of this research was to supply empirical criteria for our and other's use when choosing an appropriate clustering algorithm. Parametric comparisons were made between hierarchical clustering algorithms on their ability to retrieve the underlying populations from multinormal mixtures. Accuracy was defined as the agreement between the obtained clusters and the underlying populations, and was measured by the statistic kappa. Algorithms using the product-moment correlation coefficient as a measure of similarity were superior to those using Euclidean distance. At high levels of coverage, accuracies were higher for standardized vs. unstandardized data sets. Average linkage using correlation had the highest accuracy values, followed by Ward's error sum of squares technique and centroid linkage (weighted pair group average linkage) using correlation. Complete linkage and single linkage algorithms were significantly less accurate than these algorithms regardless of the measure of similarity used.